Computerized system and method for automatically detecting and rendering highlights from streaming videos

ABSTRACT

Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for automatically detecting and rendering highlights from streaming videos in real-time. As a streaming video is being broadcast over the Internet, the disclosed systems and methods determine each type of scene from the streaming video, and automatically score highlight scenes. The scored highlight scenes are then communicated to users as compiled video segments, which can be over any type of channel or platform accessible to a user&#39;s device and network that enables content rendering and user interaction.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority fromco-pending U.S. patent application Ser. No. 15/088,792, filed Apr. 1,2016, entitled COMPUTERIZED SYSTEM AND METHOD FOR AUTOMATICALLYDETECTING AND RENDERING HIGHLIGHTS FROM STREAMING VIDEOS, the contentsof which is hereby incorporated by reference.

This application includes material that is subject to copyrightprotection. The copyright owner has no objection to the facsimilereproduction by anyone of the patent disclosure, as it appears in thePatent and Trademark Office files or records, but otherwise reserves allcopyright rights whatsoever.

FIELD

The present disclosure relates generally to improving the performance ofcontent generating, searching, providing and/or hosting computer systemsand/or platforms by modifying the capabilities and providing non-nativefunctionality to such systems and/or platforms for automaticallydetecting and rendering highlights from streaming game videos inreal-time.

SUMMARY

The present disclosure provides novel systems and methods for automatic,real-time identification and creation of video clips from streamingvideo. According to some embodiments, the disclosed systems and methodsemploy a novel cascade prediction model, referred to, for purposes ofthis disclosure, as a scene-highlight classifier having a sceneclassifier sub-part and a highlight classifier sub-part. The sceneclassifier works by analyzing frames of streaming video (or segments) inorder to determine a type of scene being received. The highlightclassifier takes as input the frames classified as a “game” scene anddetermines a score for each sequence of frames of the game scene.According to some embodiments, the game scenes of the streaming videothat satisfy a highlight threshold are identified for communication to auser or broadcast to a plurality of users over the Internet.

Currently, automatically detecting highlights from streaming video is anextremely challenging and cost-ineffective task. Conventional systems,services and platforms are unable to identify and compile (or evenextract) highlights (or scenes of interest) from streaming media becausethey are unable to perform the necessary computational steps inreal-time (e.g., without user input) while the video is being broadcast.In fact, existing systems are only able to generate highlights of videocontent with human editors after a game has ended (e.g., after thestream has concluded). There is no current online system or mechanismfor determining and outputting “on-the-fly” segmentation of streamingmedia as the media arrives.

The present disclosure addresses the existing shortcomings in the art byproviding automatic systems and methods that label scenes from streamingmedia and score those scenes classified as a “highlight” in real-time,which can then be used to generate short-form videos of game highlightsand/or summaries.

In accordance with one or more embodiments, a method is disclosed forautomatically analyzing online streaming media in order to automaticallyidentify, score and create video clips (or segments) from the streamingvideo. The automatic creation of video clips from the streaming mediaoccurs in real-time based on the disclosed cascaded prediction modelingthat interprets the attributes of incoming streaming media andascertains the type of the content being streamed. Based on thisanalysis, a short-form video file can be created that comprises only thecontent from the streaming media corresponding to highlights of thestream.

In accordance with one or more embodiments, a non-transitorycomputer-readable storage medium is provided, the non-transitorycomputer-readable storage medium tangibly storing thereon, or havingtangibly encoded thereon, computer readable instructions that whenexecuted cause at least one processor to perform a method forautomatically detecting and rendering highlights from streaming gamevideos in real-time.

In accordance with one or more embodiments, a system is provided thatcomprises one or more computing devices configured to providefunctionality in accordance with such embodiments. In accordance withone or more embodiments, functionality is embodied in steps of a methodperformed by at least one computing device. In accordance with one ormore embodiments, program code (or program logic) executed by aprocessor(s) of a computing device to implement functionality inaccordance with one or more such embodiments is embodied in, by and/oron a non-transitory computer-readable medium.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of thedisclosure will be apparent from the following description ofembodiments as illustrated in the accompanying drawings, in whichreference characters refer to the same parts throughout the variousviews. The drawings are not necessarily to scale, emphasis instead beingplaced upon illustrating principles of the disclosure:

FIG. 1 is a schematic diagram illustrating an example of a networkwithin which the systems and methods disclosed herein could beimplemented according to some embodiments of the present disclosure;

FIG. 2 depicts is a schematic diagram illustrating an example of clientdevice in accordance with some embodiments of the present disclosure;

FIG. 3 is a schematic block diagram illustrating components of anexemplary system in accordance with embodiments of the presentdisclosure;

FIGS. 4A-4B are flowcharts illustrating steps performed in accordancewith some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating steps performed in accordance withsome embodiments of the present disclosure;

FIG. 6 is a diagram of an exemplary example of a non-limiting embodimentin accordance with some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating steps performed in accordance withsome embodiments of the present disclosure; and

FIG. 8 is a block diagram illustrating the architecture of an exemplaryhardware device in accordance with one or more embodiments of thepresent disclosure.

DESCRIPTION OF EMBODIMENTS

The present disclosure will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, certain example embodiments. Subjectmatter may, however, be embodied in a variety of different forms and,therefore, covered or claimed subject matter is intended to be construedas not being limited to any example embodiments set forth herein;example embodiments are provided merely to be illustrative. Likewise, areasonably broad scope for claimed or covered subject matter isintended. Among other things, for example, subject matter may beembodied as methods, devices, components, or systems. Accordingly,embodiments may, for example, take the form of hardware, software,firmware or any combination thereof (other than software per se). Thefollowing detailed description is, therefore, not intended to be takenin a limiting sense.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in one embodiment” as used herein does notnecessarily refer to the same embodiment and the phrase “in anotherembodiment” as used herein does not necessarily refer to a differentembodiment. It is intended, for example, that claimed subject matterinclude combinations of example embodiments in whole or in part.

In general, terminology may be understood at least in part from usage incontext. For example, terms, such as “and”, “or”, or “and/or,” as usedherein may include a variety of meanings that may depend at least inpart upon the context in which such terms are used. Typically, “or” ifused to associate a list, such as A, B or C, is intended to mean A, B,and C, here used in the inclusive sense, as well as A, B or C, here usedin the exclusive sense. In addition, the term “one or more” as usedherein, depending at least in part upon context, may be used to describeany feature, structure, or characteristic in a singular sense or may beused to describe combinations of features, structures or characteristicsin a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again,may be understood to convey a singular usage or to convey a pluralusage, depending at least in part upon context. In addition, the term“based on” may be understood as not necessarily intended to convey anexclusive set of factors and may, instead, allow for existence ofadditional factors not necessarily expressly described, again, dependingat least in part on context.

The present disclosure is described below with reference to blockdiagrams and operational illustrations of methods and devices. It isunderstood that each block of the block diagrams or operationalillustrations, and combinations of blocks in the block diagrams oroperational illustrations, can be implemented by means of analog ordigital hardware and computer program instructions. These computerprogram instructions can be provided to a processor of a general purposecomputer to alter its function as detailed herein, a special purposecomputer, ASIC, or other programmable data processing apparatus, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, implement thefunctions/acts specified in the block diagrams or operational block orblocks. In some alternate implementations, the functions/acts noted inthe blocks can occur out of the order noted in the operationalillustrations. For example, two blocks shown in succession can in factbe executed substantially concurrently or the blocks can sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved.

These computer program instructions can be provided to a processor of: ageneral purpose computer to alter its function to a special purpose; aspecial purpose computer; ASIC; or other programmable digital dataprocessing apparatus, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, implement the functions/acts specified in the block diagramsor operational block or blocks, thereby transforming their functionalityin accordance with embodiments herein.

For the purposes of this disclosure a computer readable medium (orcomputer-readable storage medium/media) stores computer data, which datacan include computer program code (or computer-executable instructions)that is executable by a computer, in machine readable form. By way ofexample, and not limitation, a computer readable medium may comprisecomputer readable storage media, for tangible or fixed storage of data,or communication media for transient interpretation of code-containingsignals. Computer readable storage media, as used herein, refers tophysical or tangible storage (as opposed to signals) and includeswithout limitation volatile and non-volatile, removable andnon-removable media implemented in any method or technology for thetangible storage of information such as computer-readable instructions,data structures, program modules or other data. Computer readablestorage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM,flash memory or other solid state memory technology, CD-ROM, DVD, orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other physical ormaterial medium which can be used to tangibly store the desiredinformation or data or instructions and which can be accessed by acomputer or processor.

For the purposes of this disclosure the term “server” should beunderstood to refer to a service point which provides processing,database, and communication facilities. By way of example, and notlimitation, the term “server” can refer to a single, physical processorwith associated communications and data storage and database facilities,or it can refer to a networked or clustered complex of processors andassociated network and storage devices, as well as operating softwareand one or more database systems and application software that supportthe services provided by the server. Servers may vary widely inconfiguration or capabilities, but generally a server may include one ormore central processing units and memory. A server may also include oneor more mass storage devices, one or more power supplies, one or morewired or wireless network interfaces, one or more input/outputinterfaces, or one or more operating systems, such as Windows Server,Mac OS X, Unix, Linux, FreeBSD, or the like.

For the purposes of this disclosure a “network” should be understood torefer to a network that may couple devices so that communications may beexchanged, such as between a server and a client device or other typesof devices, including between wireless devices coupled via a wirelessnetwork, for example. A network may also include mass storage, such asnetwork attached storage (NAS), a storage area network (SAN), or otherforms of computer or machine readable media, for example. A network mayinclude the Internet, one or more local area networks (LANs), one ormore wide area networks (WANs), wire-line type connections, wirelesstype connections, cellular or any combination thereof. Likewise,sub-networks, which may employ differing architectures or may becompliant or compatible with differing protocols, may interoperatewithin a larger network. Various types of devices may, for example, bemade available to provide an interoperable capability for differingarchitectures or protocols. As one illustrative example, a router mayprovide a link between otherwise separate and independent LANs.

A communication link or channel may include, for example, analogtelephone lines, such as a twisted wire pair, a coaxial cable, full orfractional digital lines including T1, T2, T3, or T4 type lines,Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines(DSLs), wireless links including satellite links, or other communicationlinks or channels, such as may be known to those skilled in the art.Furthermore, a computing device or other related electronic devices maybe remotely coupled to a network, such as via a wired or wireless lineor link, for example.

For purposes of this disclosure, a “wireless network” should beunderstood to couple client devices with a network. A wireless networkmay employ stand-alone ad-hoc networks, mesh networks, Wireless LAN(WLAN) networks, cellular networks, or the like. A wireless network mayfurther include a system of terminals, gateways, routers, or the likecoupled by wireless radio links, or the like, which may move freely,randomly or organize themselves arbitrarily, such that network topologymay change, at times even rapidly.

A wireless network may further employ a plurality of network accesstechnologies, including Wi-Fi, Long Term Evolution (LTE), WLAN, WirelessRouter (WR) mesh, or 2nd, 3rd, or 4th generation (2G, 3G, or 4G)cellular technology, or the like. Network access technologies may enablewide area coverage for devices, such as client devices with varyingdegrees of mobility, for example.

For example, a network may enable RF or wireless type communication viaone or more network access technologies, such as Global System forMobile communication (GSM), Universal Mobile Telecommunications System(UMTS), General Packet Radio Services (GPRS), Enhanced Data GSMEnvironment (EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced,Wideband Code Division Multiple Access (WCDMA), Bluetooth, 802.11b/g/n,or the like. A wireless network may include virtually any type ofwireless communication mechanism by which signals may be communicatedbetween devices, such as a client device or a computing device, betweenor within a network, or the like.

A computing device may be capable of sending or receiving signals, suchas via a wired or wireless network, or may be capable of processing orstoring signals, such as in memory as physical memory states, and may,therefore, operate as a server. Thus, devices capable of operating as aserver may include, as examples, dedicated rack-mounted servers, desktopcomputers, laptop computers, set top boxes, integrated devices combiningvarious features, such as two or more features of the foregoing devices,or the like. Servers may vary widely in configuration or capabilities,but generally a server may include one or more central processing unitsand memory. A server may also include one or more mass storage devices,one or more power supplies, one or more wired or wireless networkinterfaces, one or more input/output interfaces, or one or moreoperating systems, such as Windows Server, Mac OS X, Unix, Linux,FreeBSD, or the like.

For purposes of this disclosure, a client (or consumer or user) devicemay include a computing device capable of sending or receiving signals,such as via a wired or a wireless network. A client device may, forexample, include a desktop computer or a portable device, such as acellular telephone, a smart phone, a display pager, a radio frequency(RF) device, an infrared (IR) device an Near Field Communication (NFC)device, a Personal Digital Assistant (PDA), a handheld computer, atablet computer, a phablet, a laptop computer, a set top box, a wearablecomputer, smart watch, an integrated or distributed device combiningvarious features, such as features of the forgoing devices, or the like.

A client device may vary in terms of capabilities or features. Claimedsubject matter is intended to cover a wide range of potentialvariations. For example, a simple smart phone, phablet or tablet mayinclude a numeric keypad or a display of limited functionality, such asa monochrome liquid crystal display (LCD) for displaying text. Incontrast, however, as another example, a web-enabled client device mayinclude a high resolution screen, one or more physical or virtualkeyboards, mass storage, one or more accelerometers, one or moregyroscopes, global positioning system (GPS) or otherlocation-identifying type capability, or a display with a high degree offunctionality, such as a touch-sensitive color 2D or 3D display, forexample.

A client device may include or may execute a variety of operatingsystems, including a personal computer operating system, such as aWindows, iOS or Linux, or a mobile operating system, such as iOS,Android, or Windows Mobile, or the like.

A client device may include or may execute a variety of possibleapplications, such as a client software application enablingcommunication with other devices, such as communicating one or moremessages, such as via email, for example Yahoo! ® Mail, short messageservice (SMS), or multimedia message service (MMS), for example Yahoo!Messenger®, including via a network, such as a social network,including, for example, Tumblr®, Facebook®, LinkedIn®, Twitter®,Flickr®, or Google+®, Instagram™, to provide only a few possibleexamples. A client device may also include or execute an application tocommunicate content, such as, for example, textual content, multimediacontent, or the like. A client device may also include or execute anapplication to perform a variety of possible tasks, such as browsing,searching, playing or displaying various forms of content, includinglocally stored or streamed video, or games (such as fantasy sportsleagues). The foregoing is provided to illustrate that claimed subjectmatter is intended to include a wide range of possible features orcapabilities.

The principles described herein may be embodied in many different forms.The present disclosure provides novel systems and methods for automatic,real-time identification and/or creation of video clips (or segments)from streaming video.

The disclosed systems and methods employ a novel cascade predictionmodel, referred to, as discussed above, a scene-highlight classifier. Asdiscussed in detail below in relation to FIGS. 4A-4B, thescene-highlight classifier is trained and/or modeled based upon anyknown or to be known machine learning modeling technique or algorithmthat leverages analyzed visual scene attributes/characteristics within atraining set of video through an applied machine-in-loop videoannotation system. The training of the scene-highlight classifierenables the disclosed systems and methods (e.g., the scene-highlightclassifier engine 300) to discard certain parts (e.g., frames) fromstreaming video in order to focus on detecting highlights from theremaining frames of the streaming video.

The scene-highlight classifier comprises two layers: a scene classifierlayer and a highlight classifier layer. As discussed in detail below,the scene-highlight classifier has a conditional cascade modelinginfrastructure based on the premise that only particular types of scenesdetermined by the scene classifier are passed on to the highlightclassifier layer.

As understood by those of skill in the art, streaming media comprisesdistinct scenes that correspond to particular types of content. Suchcontent types include, but are not limited to, scenes where acommentator is speaking, scenes depicting game play, scenes depictingimages of a game player or his/her avatar or digital likeness, scenesdepicting the audience, and the like.

As discussed herein, the scene classifier analyzes incoming (and stored)frames of streaming video (or segments) in order to determine a type ofscene being received within the stream. The scene classifier aims todiscriminate game scenes (e.g., scenes that depict game play) fromnon-game scenes (e.g., scenes that comprise content associated with acommentator, game player, audience, and the like). The highlightclassifier takes as input the sequence of frames classified as a “game”scene and determines a score. According to some embodiments, the gamescenes of the streaming video that satisfy a highlight threshold (orfall within a range, as discussed below) are determined to be a“highlight” and identified for communication to a user or broadcast to aplurality of users over the Internet.

According to some embodiments, the highlight threshold (or range)ensures that the game scenes comprise content associated with adistinctive set of predetermined visual parameters. Such visualparameters can include, but are not limited to, a threshold satisfyingamount of activity occurring during the segment, a threshold satisfyingvariation of pixel attributes (e.g., a purse of bright light triggeredby activity in the segment), a displayed game status (e.g., anindication that an enemy has been killed or a person has scored), andthe like.

By way of a non-limiting example, FIG. 6 illustrates a non-limitingembodiment of the instant disclosure. In the example, video stream 600is received. The stream 600 comprises 8 frames—numbered 1-8. Frames 1-3,item 602, comprise content showing a commentator welcoming the viewersto the live broadcast. Frame 4, item 608, comprises a scene transitionor shot boundary within the video stream 600, such as, for example, acut between video frames 3 and 5, fade in/out between frames 3 and 5,dissolve or wipe effect(s), and/or any other type of known or to beknown effect that transitions between scenes of a video file. Frames5-7, item 604, comprise content showing game play—for example, livestreaming footage of two players playing an online game and one player“killing” the other player. And, Frame 8, item 610, comprises contentindicating the end of the stream—for example, a fade-to-blacktransition.

The disclosed systems and methods can analyze the incoming stream 600 inreal-time in order to determine which sequence of frames (or scene) ofthe stream correspond to a game scene. As detailed below in relation toFIGS. 3-5, as the frames of the stream 600 are received (and/or storedin memory or a database/datastore), the scene classifier of thescene-highlight classifier analyzes the frames to determine what type ofcontent is being relayed by each frame or sequence of frames. If thescene classifier determines that the scene(s) is related to game-play,then the scene(s) is passed to the highlight classifier, which scoresthe scene in order to determine whether the game play is an actualhighlight.

As with the example of FIG. 6, item 602 corresponds to a scene of thecommentator speaking. Item 604 corresponds to a scene of one playerscoring on the other player (e.g., “killing” the other player within theconstruct of the game). Therefore, according to some embodiments of thepresent disclosure, only scene 604 is passed to the highlightclassifier.

In some embodiments, the scenes 602 and 604 are labeled based on theanalysis by the scene classifier. In some embodiments, such labelsprovide an indication as to not only the type of content depicted uponrendering of the frames of the scene, but also the length (and/orbeginning and end) of the scene. In some embodiments, items 608 and 610,Frames 4 and 8 respectively, can be determined by the scene-highlightclassifier implementing any known or to be known media frame analysisalgorithm or scheme technique for determining differences betweenadjacent frames. In such embodiments, these labeled frames can serve asdesignators for the starting and/or stopping of particular scenes withinthe stream 600.

Continuing with the above example, the highlight classifier analyzes thescene and scores the content of the scene 604. For example, as discussedin more detail below, highlight classifier can execute any known or tobe known type of image or content recognition model or algorithm thatcan identify the depicted content of each frame of scene 604 (frames5-7) and calculate a score for the activity occurring within, duringand/or between frames 5-7. Since frames 5-7 depict one player scoringover another, for example, such activity would be result in a scoresatisfying the highlight threshold, therefore, scene 604 would belabeled as a “highlight.”

In some embodiments, as discussed in more detail below, the identifiedframes corresponding to the determined “highlight” scene can beextracted, identified or otherwise utilized for creation of a short-formvideo clip or segment. In some embodiments, such creation of a highlightvideo segment can involve, but not limited to, generating (or creatingor extracting) a highlight video segment from the frames of the stream600 using any known or to be known frame/segment transformationtechnique, such as, but not limited to, imagemagick and gifsiclelibraries, to name a few examples. For example, scene 604 can betransformed into a highlight video segment that is formatted as agraphics interchange format (GIF) file. Such GIF file can then becommunicated to a requesting user and/or provided on an online platformthat enables users to view highlights of on-going or past game play.

The disclosed systems and methods can be implemented for any type ofcontent item or streaming media, including, but not limited to, video,audio, images, text, and/or any other type of multimedia content. Whilethe discussion herein will focus on streaming video and identificationof video frames/segments/clips within such stream, it should not beconstrued as limiting, as any type of content or multimedia content,whether known or to be known, can be utilized without departing from thescope of the instant disclosure.

As discussed in more detail below at least in relation to FIG. 7,according to some embodiments, information associated with or derivedfrom identified and/or created highlight video segments, as discussedherein, can be used for monetization purposes and targeted advertisingwhen providing, delivering, sharing or enabling access to the streamingmedia and/or created highlight video segments (e.g., on Yahoo!'seSports® platform). Providing targeted advertising to users associatedwith such discovered content can lead to an increased click-through rate(CTR) of such ads and/or an increase in the advertiser's return oninvestment (ROI) for serving such content provided by third parties(e.g., digital advertisement content provided by an advertiser, wherethe advertiser can be a third party advertiser, or an entity directlyassociated with or hosting the systems and methods discussed herein).

Certain embodiments will now be described in greater detail withreference to the figures. In general, with reference to FIG. 1, a system100 in accordance with an embodiment of the present disclosure is shown.FIG. 1 shows components of a general environment in which the systemsand methods discussed herein may be practiced. Not all the componentsmay be required to practice the disclosure, and variations in thearrangement and type of the components may be made without departingfrom the spirit or scope of the disclosure. As shown, system 100 of FIG.1 includes local area networks (“LANs”)/wide area networks(“WANs”)—network 105, wireless network 110, mobile devices (clientdevices) 102-104 and client device 101. FIG. 1 additionally includes avariety of servers, such as content server 106, application (or “App”)server 108, search server 120 and advertising (“ad”) server 130.

One embodiment of mobile devices 102-104 is described in more detailbelow. Generally, however, mobile devices 102-104 may include virtuallyany portable computing device capable of receiving and sending a messageover a network, such as network 105, wireless network 110, or the like.Mobile devices 102-104 may also be described generally as client devicesthat are configured to be portable. Thus, mobile devices 102-104 mayinclude virtually any portable computing device capable of connecting toanother computing device and receiving information. Such devices includemulti-touch and portable devices such as, cellular telephones, smartphones, display pagers, radio frequency (RF) devices, infrared (IR)devices, Personal Digital Assistants (PDAs), handheld computers, laptopcomputers, wearable computers, smart watch, tablet computers, phablets,integrated devices combining one or more of the preceding devices, andthe like. As such, mobile devices 102-104 typically range widely interms of capabilities and features. For example, a cell phone may have anumeric keypad and a few lines of monochrome LCD display on which onlytext may be displayed. In another example, a web-enabled mobile devicemay have a touch sensitive screen, a stylus, and an HD display in whichboth text and graphics may be displayed.

A web-enabled mobile device may include a browser application that isconfigured to receive and to send web pages, web-based messages, and thelike. The browser application may be configured to receive and displaygraphics, text, multimedia, and the like, employing virtually any webbased language, including a wireless application protocol messages(WAP), and the like. In one embodiment, the browser application isenabled to employ Handheld Device Markup Language (HDML), WirelessMarkup Language (WML), WMLScript, JavaScript, Standard GeneralizedMarkup Language (SMGL), HyperText Markup Language (HTML), eXtensibleMarkup Language (XML), and the like, to display and send a message.

Mobile devices 102-104 also may include at least one client applicationthat is configured to receive content from another computing device. Theclient application may include a capability to provide and receivetextual content, graphical content, audio content, and the like. Theclient application may further provide information that identifiesitself, including a type, capability, name, and the like. In oneembodiment, mobile devices 102-104 may uniquely identify themselvesthrough any of a variety of mechanisms, including a phone number, MobileIdentification Number (MIN), an electronic serial number (ESN), or othermobile device identifier.

In some embodiments, mobile devices 102-104 may also communicate withnon-mobile client devices, such as client device 101, or the like. Inone embodiment, such communications may include sending and/or receivingmessages, searching for, viewing and/or sharing photographs, audioclips, video clips, or any of a variety of other forms ofcommunications. Client device 101 may include virtually any computingdevice capable of communicating over a network to send and receiveinformation. The set of such devices may include devices that typicallyconnect using a wired or wireless communications medium such as personalcomputers, multiprocessor systems, microprocessor-based or programmableconsumer electronics, network PCs, or the like. Thus, client device 101may also have differing capabilities for displaying navigable views ofinformation.

Client devices 101-104 computing device may be capable of sending orreceiving signals, such as via a wired or wireless network, or may becapable of processing or storing signals, such as in memory as physicalmemory states, and may, therefore, operate as a server. Thus, devicescapable of operating as a server may include, as examples, dedicatedrack-mounted servers, desktop computers, laptop computers, set topboxes, integrated devices combining various features, such as two ormore features of the foregoing devices, or the like.

Wireless network 110 is configured to couple mobile devices 102-104 andits components with network 105. Wireless network 110 may include any ofa variety of wireless sub-networks that may further overlay stand-alonead-hoc networks, and the like, to provide an infrastructure-orientedconnection for mobile devices 102-104. Such sub-networks may includemesh networks, Wireless LAN (WLAN) networks, cellular networks, and thelike.

Network 105 is configured to couple content server 106, applicationserver 108, or the like, with other computing devices, including, clientdevice 101, and through wireless network 110 to mobile devices 102-104.Network 105 is enabled to employ any form of computer readable media forcommunicating information from one electronic device to another. Also,network 105 can include the Internet in addition to local area networks(LANs), wide area networks (WANs), direct connections, such as through auniversal serial bus (USB) port, other forms of computer-readable media,or any combination thereof. On an interconnected set of LANs, includingthose based on differing architectures and protocols, a router acts as alink between LANs, enabling messages to be sent from one to another,and/or other computing devices.

Within the communications networks utilized or understood to beapplicable to the present disclosure, such networks will employ variousprotocols that are used for communication over the network. Signalpackets communicated via a network, such as a network of participatingdigital communication networks, may be compatible with or compliant withone or more protocols. Signaling formats or protocols employed mayinclude, for example, TCP/IP, UDP, QUIC (Quick UDP Internet Connection),DECnet, NetBEUl, IPX, APPLETALK™, or the like. Versions of the InternetProtocol (IP) may include IPv4 or IPv6. The Internet refers to adecentralized global network of networks. The Internet includes localarea networks (LANs), wide area networks (WANs), wireless networks, orlong haul public networks that, for example, allow signal packets to becommunicated between LANs. Signal packets may be communicated betweennodes of a network, such as, for example, to one or more sites employinga local network address. A signal packet may, for example, becommunicated over the Internet from a user site via an access nodecoupled to the Internet. Likewise, a signal packet may be forwarded vianetwork nodes to a target site coupled to the network via a networkaccess node, for example. A signal packet communicated via the Internetmay, for example, be routed via a path of gateways, servers, etc. thatmay route the signal packet in accordance with a target address andavailability of a network path to the target address.

According to some embodiments, the present disclosure may also beutilized within or accessible to an electronic social networking site. Asocial network refers generally to an electronic network of individuals,such as acquaintances, friends, family, colleagues, or co-workers, whichare coupled via a communications network or via a variety ofsub-networks. Potentially, additional relationships may subsequently beformed as a result of social interaction via the communications networkor sub-networks. In some embodiments, multi-modal communications mayoccur between members of the social network. Individuals within one ormore social networks may interact or communication with other members ofa social network via a variety of devices. Multi-modal communicationtechnologies refers to a set of technologies that permit interoperablecommunication across multiple devices or platforms, such as cell phones,smart phones, tablet computing devices, phablets, personal computers,televisions, set-top boxes, SMS/MMS, email, instant messenger clients,forums, social networking sites, or the like.

In some embodiments, the disclosed networks 110 and/or 105 may comprisea content distribution network(s). A “content delivery network” or“content distribution network” (CDN) generally refers to a distributedcontent delivery system that comprises a collection of computers orcomputing devices linked by a network or networks. A CDN may employsoftware, systems, protocols or techniques to facilitate variousservices, such as storage, caching, communication of content, orstreaming media or applications. A CDN may also enable an entity tooperate or manage another's site infrastructure, in whole or in part.

The content server 106 may include a device that includes aconfiguration to provide content via a network to another device. Acontent server 106 may, for example, host a site or service, such asstreaming media site/service (e.g., Yahoo! eSports®, YouTube®), a gamingsite, an email platform or social networking site, or a personal usersite (such as a blog, vlog, online dating site, and the like). A contentserver 106 may also host a variety of other sites, including, but notlimited to business sites, educational sites, dictionary sites,encyclopedia sites, wikis, financial sites, government sites, and thelike. Devices that may operate as content server 106 include personalcomputers desktop computers, multiprocessor systems,microprocessor-based or programmable consumer electronics, network PCs,servers, and the like.

Content server 106 can further provide a variety of services thatinclude, but are not limited to, streaming and/or downloading mediaservices, search services, email services, photo services, web services,social networking services, news services, third-party services, audioservices, video services, instant messaging (IM) services, SMS services,MMS services, FTP services, voice over IP (VOIP) services, or the like.Such services, for example a video application and/or video platform,can be provided via the application server 108, whereby a user is ableto utilize such service upon the user being authenticated, verified oridentified by the service. Examples of content may include images, text,audio, video, or the like, which may be processed in the form ofphysical signals, such as electrical signals, for example, or may bestored in memory, as physical states, for example.

An ad server 130 comprises a server that stores online advertisementsfor presentation to users. “Ad serving” refers to methods used to placeonline advertisements on websites, in applications, or other placeswhere users are more likely to see them, such as during an onlinesession or during computing platform use, for example. Variousmonetization techniques or models may be used in connection withsponsored advertising, including advertising associated with user. Suchsponsored advertising includes monetization techniques includingsponsored search advertising, non-sponsored search advertising,guaranteed and non-guaranteed delivery advertising, adnetworks/exchanges, ad targeting, ad serving and ad analytics. Suchsystems can incorporate near instantaneous auctions of ad placementopportunities during web page creation, (in some cases in less than 500milliseconds) with higher quality ad placement opportunities resultingin higher revenues per ad. That is advertisers will pay higheradvertising rates when they believe their ads are being placed in oralong with highly relevant content that is being presented to users.Reductions in the time needed to quantify a high quality ad placementoffers ad platforms competitive advantages. Thus higher speeds and morerelevant context detection improve these technological fields.

For example, a process of buying or selling online advertisements mayinvolve a number of different entities, including advertisers,publishers, agencies, networks, or developers. To simplify this process,organization systems called “ad exchanges” may associate advertisers orpublishers, such as via a platform to facilitate buying or selling ofonline advertisement inventory from multiple ad networks. “Ad networks”refers to aggregation of ad space supply from publishers, such as forprovision en masse to advertisers. For web portals like Yahoo! ®,advertisements may be displayed on web pages or in apps resulting from auser-defined search based at least in part upon one or more searchterms. Advertising may be beneficial to users, advertisers or webportals if displayed advertisements are relevant to interests of one ormore users. Thus, a variety of techniques have been developed to inferuser interest, user intent or to subsequently target relevantadvertising to users. One approach to presenting targeted advertisementsincludes employing demographic characteristics (e.g., age, income, sex,occupation, etc.) for predicting user behavior, such as by group.Advertisements may be presented to users in a targeted audience based atleast in part upon predicted user behavior(s).

Another approach includes profile-type ad targeting. In this approach,user profiles specific to a user may be generated to model userbehavior, for example, by tracking a user's path through a web site ornetwork of sites, and compiling a profile based at least in part onpages or advertisements ultimately delivered. A correlation may beidentified, such as for user purchases, for example. An identifiedcorrelation may be used to target potential purchasers by targetingcontent or advertisements to particular users. During presentation ofadvertisements, a presentation system may collect descriptive contentabout types of advertisements presented to users. A broad range ofdescriptive content may be gathered, including content specific to anadvertising presentation system. Advertising analytics gathered may betransmitted to locations remote to an advertising presentation systemfor storage or for further evaluation. Where advertising analyticstransmittal is not immediately available, gathered advertising analyticsmay be stored by an advertising presentation system until transmittal ofthose advertising analytics becomes available.

Servers 106, 108, 120 and 130 may be capable of sending or receivingsignals, such as via a wired or wireless network, or may be capable ofprocessing or storing signals, such as in memory as physical memorystates. Devices capable of operating as a server may include, asexamples, dedicated rack-mounted servers, desktop computers, laptopcomputers, set top boxes, integrated devices combining various features,such as two or more features of the foregoing devices, or the like.Servers may vary widely in configuration or capabilities, but generally,a server may include one or more central processing units and memory. Aserver may also include one or more mass storage devices, one or morepower supplies, one or more wired or wireless network interfaces, one ormore input/output interfaces, or one or more operating systems, such asWindows Server, Mac OS X, Unix, Linux, FreeBSD, or the like.

In some embodiments, users are able to access services provided byservers 106, 108, 120 and/or 130. This may include in a non-limitingexample, game servers, authentication servers, search servers, emailservers, social networking services servers, SMS servers, IM servers,MMS servers, exchange servers, photo-sharing services servers, andtravel services servers, via the network 105 using their various devices101-104. In some embodiments, applications, such as a gamingapplication, a streaming video application, blog, photo storage/sharingapplication or social networking application, can be hosted by theapplication server 108 (or content server 106, search server 120 and thelike). Thus, the application server 108 can store various types ofapplications and application related information including applicationdata and user profile information (e.g., identifying and behavioralinformation associated with a user). It should also be understood thatcontent server 106 can also store various types of data related to thecontent and services provided by content server 106 in an associatedcontent database 107, as discussed in more detail below. Embodimentsexist where the network 105 is also coupled with/connected to a TrustedSearch Server (TSS) which can be utilized to render content inaccordance with the embodiments discussed herein. Embodiments existwhere the TSS functionality can be embodied within servers 106, 108, 120and/or 130.

Moreover, although FIG. 1 illustrates servers 106, 108, 120 and 130 assingle computing devices, respectively, the disclosure is not solimited. For example, one or more functions of servers 106, 108, 120and/or 130 may be distributed across one or more distinct computingdevices. Moreover, in one embodiment, servers 106, 108, 120 and/or 130may be integrated into a single computing device, without departing fromthe scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating a client device showing anexample embodiment of a client device that may be used within thepresent disclosure. Client device 200 may include many more or lesscomponents than those shown in FIG. 2. However, the components shown aresufficient to disclose an illustrative embodiment for implementing thepresent disclosure. Client device 200 may represent, for example, clientdevices discussed above in relation to FIG. 1.

As shown in the figure, Client device 200 includes a processing unit(CPU) 222 in communication with a mass memory 230 via a bus 224. Clientdevice 200 also includes a power supply 226, one or more networkinterfaces 250, an audio interface 252, a display 254, a keypad 256, anilluminator 258, an input/output interface 260, a haptic interface 262,an optional global positioning systems (GPS) receiver 264 and acamera(s) or other optical, thermal or electromagnetic sensors 266.Device 200 can include one camera/sensor 266, or a plurality ofcameras/sensors 266, as understood by those of skill in the art. Thepositioning of the camera(s)/sensor(s) 266 on device 200 can change perdevice 200 model, per device 200 capabilities, and the like, or somecombination thereof.

Power supply 226 provides power to Client device 200. A rechargeable ornon-rechargeable battery may be used to provide power. The power mayalso be provided by an external power source, such as an AC adapter or apowered docking cradle that supplements and/or recharges a battery.

Client device 200 may optionally communicate with a base station (notshown), or directly with another computing device. Network interface 250includes circuitry for coupling Client device 200 to one or morenetworks, and is constructed for use with one or more communicationprotocols and technologies as discussed above. Network interface 250 issometimes known as a transceiver, transceiving device, or networkinterface card (NIC).

Audio interface 252 is arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 252 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others and/or generate an audio acknowledgementfor some action. Display 254 may be a liquid crystal display (LCD), gasplasma, light emitting diode (LED), or any other type of display usedwith a computing device. Display 254 may also include a touch sensitivescreen arranged to receive input from an object such as a stylus or adigit from a human hand.

Keypad 256 may comprise any input device arranged to receive input froma user. For example, keypad 256 may include a push button numeric dial,or a keyboard. Keypad 256 may also include command buttons that areassociated with selecting and sending images. Illuminator 258 mayprovide a status indication and/or provide light. Illuminator 258 mayremain active for specific periods of time or in response to events. Forexample, when illuminator 258 is active, it may backlight the buttons onkeypad 256 and stay on while the client device is powered. Also,illuminator 258 may backlight these buttons in various patterns whenparticular actions are performed, such as dialing another client device.Illuminator 258 may also cause light sources positioned within atransparent or translucent case of the client device to illuminate inresponse to actions.

Client device 200 also comprises input/output interface 260 forcommunicating with external devices, such as a headset, or other inputor output devices not shown in FIG. 2. Input/output interface 260 canutilize one or more communication technologies, such as USB, infrared,Bluetooth™, or the like. Haptic interface 262 is arranged to providetactile feedback to a user of the client device. For example, the hapticinterface may be employed to vibrate client device 200 in a particularway when the Client device 200 receives a communication from anotheruser.

Optional GPS transceiver 264 can determine the physical coordinates ofClient device 200 on the surface of the Earth, which typically outputs alocation as latitude and longitude values. GPS transceiver 264 can alsoemploy other geo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), E-OTD, CI, SAI, ETA, BSS or thelike, to further determine the physical location of Client device 200 onthe surface of the Earth. It is understood that under differentconditions, GPS transceiver 264 can determine a physical location withinmillimeters for Client device 200; and in other cases, the determinedphysical location may be less precise, such as within a meter orsignificantly greater distances. In one embodiment, however, Clientdevice may through other components, provide other information that maybe employed to determine a physical location of the device, includingfor example, a MAC address, Internet Protocol (IP) address, or the like.

Mass memory 230 includes a RAM 232, a ROM 234, and other storage means.Mass memory 230 illustrates another example of computer storage mediafor storage of information such as computer readable instructions, datastructures, program modules or other data. Mass memory 230 stores abasic input/output system (“BIOS”) 240 for controlling low-leveloperation of Client device 200. The mass memory also stores an operatingsystem 241 for controlling the operation of Client device 200. It willbe appreciated that this component may include a general purposeoperating system such as a version of UNIX, or LINUX™, or a specializedclient communication operating system such as Windows Client™, or theSymbian® operating system. The operating system may include, orinterface with a Java virtual machine module that enables control ofhardware components and/or operating system operations via Javaapplication programs.

Memory 230 further includes one or more data stores, which can beutilized by Client device 200 to store, among other things, applications242 and/or other data. For example, data stores may be employed to storeinformation that describes various capabilities of Client device 200.The information may then be provided to another device based on any of avariety of events, including being sent as part of a header during acommunication, sent upon request, or the like. At least a portion of thecapability information may also be stored on a disk drive or otherstorage medium (not shown) within Client device 200.

Applications 242 may include computer executable instructions which,when executed by Client device 200, transmit, receive, and/or otherwiseprocess audio, video, images, and enable telecommunication with a serverand/or another user of another client device. Other examples ofapplication programs or “apps” in some embodiments include browsers,calendars, contact managers, task managers, transcoders, photomanagement, database programs, word processing programs, securityapplications, spreadsheet programs, games, search programs, and soforth. Applications 242 may further include search client 245 that isconfigured to send, to receive, and/or to otherwise process a searchquery and/or search result using any known or to be known communicationprotocols. Although a single search client 245 is illustrated it shouldbe clear that multiple search clients may be employed. For example, onesearch client may be configured to enter a search query message, whereanother search client manages search results, and yet another searchclient is configured to manage serving advertisements, IMs, emails, andother types of known messages, or the like.

Having described the components of the general architecture employedwithin the disclosed systems and methods, the components' generaloperation with respect to the disclosed systems and methods will now bedescribed below.

FIG. 3 is a block diagram illustrating the components for performing thesystems and methods discussed herein. FIG. 3 includes a scene-highlightclassifier engine 300, network 315 and database 320. The scene-highlightclassifier engine 300 can be a special purpose machine or processor andcould be hosted by an application server, game server, content server,social networking server, web server, search server, content provider,email service provider, ad server, user's computing device, and thelike, or any combination thereof.

According to some embodiments, scene-highlight classifier engine 300 canbe embodied as a stand-alone application that executes on a user device.In some embodiments, the scene-highlight classifier engine 300 canfunction as an application installed on the user's device, and in someembodiments, such application can be a web-based application accessed bythe user device over a network. In some embodiments, the scene-highlightclassifier engine 300 can be installed as an augmenting script, programor application to another media application (e.g., Yahoo! eSports®,Yahoo! Video®, Hulu®, and the like).

The database 320 can be any type of database or memory, and can beassociated with a content server on a network (e.g., content server 106,search server 120 or application server 108 from FIG. 1) or a user'sdevice (e.g., device 101-104 or device 200 from FIGS. 1-2). Database 320comprises a dataset of data and metadata associated with local and/ornetwork information related to users, services, applications, content(e.g., video) and the like. Such information can be stored and indexedin the database 320 independently and/or as a linked or associateddataset. As discussed above, it should be understood that the data (andmetadata) in the database 320 can be any type of information and type,whether known or to be known, without departing from the scope of thepresent disclosure.

According to some embodiments, database 320 can store data for users,e.g., user data. According to some embodiments, the stored user data caninclude, but is not limited to, information associated with a user'sprofile, user interests, user behavioral information, user attributes,user preferences or settings, user demographic information, userlocation information, user biographic information, and the like, or somecombination thereof. In some embodiments, the user data can alsoinclude, for purposes creating, streaming, recommending, renderingand/or delivering videos, user device information, including, but notlimited to, device identifying information, device capabilityinformation, voice/data carrier information, Internet Protocol (IP)address, applications installed or capable of being installed orexecuted on such device, and/or any, or some combination thereof. Itshould be understood that the data (and metadata) in the database 320can be any type of information related to a user, content, a device, anapplication, a service provider, a content provider, whether known or tobe known, without departing from the scope of the present disclosure.

According to some embodiments, database 320 can store data and metadataassociated with video content from an assortment of media and/or serviceproviders and/or platforms (e.g., game content and/or game console orplatform content/information). For example, the information can berelated to, but not limited to, content type of the video, a categoryassociated with the video, information associated with the pixels andframes of the videos, information associated with the provider of thevideo, information associated with the players involved in the video,and any other type of known or to be known attribute or featureassociated with a video file. Additionally, the video information indatabase 320 for each video can comprise, but is not limited to,attributes including, but not limited to, popularity of the video,quality of the video, recency of the video (when it was published,shared, edited and the like), and the like. Such factors can be derivedfrom information provided by the user, a service provider (i.e., Yahoo!®or Tumblr®), by the content/service providers providing video content(e.g., Yahoo! eSports®, ESPN®, ABC Sports®, Netflix®, Hulu®, YouTube®),or by other third party services (e.g., rottentomatoes.com, IMDB™,Facebook®, Twitter® and the like), or some combination thereof.

According to some embodiments, as such video information is received, itcan be stored in database 320 as a n-dimensional vector (or featurevector) representation for each video and/or for each frame of thevideo, where the information associated with the video can be translatedas a node on the n-dimensional vector. Database 320 can store and indexvideo information in database 320 as linked set of video data andmetadata, where the data and metadata relationship can be stored as then-dimensional vector. Such storage can be realized through any known orto be known vector or array storage, including but not limited to, ahash tree, queue, stack, VList, or any other type of known or to beknown dynamic memory allocation technique or technology. While thestorage discussion above involves vector analysis of streaming video andvideo information associated therewith, the stored video information canbe analyzed, stored and indexed according to any known or to be knowncomputational analysis technique or algorithm, such as, but not limitedto, cluster analysis, data mining, Bayesian network analysis, HiddenMarkov models, artificial neural network analysis, logical model and/ortree analysis, and the like.

For purposes of the present disclosure, as discussed above, videos(which are stored and located in database 320) as a whole are discussedwithin some embodiments; however, it should not be construed to limitthe applications of the systems and methods discussed herein. That is,while reference is made throughout the instant disclosure to videos(e.g., streaming and/or downloadable videos associated with live onlinegaming), other forms of user generated content and associatedinformation, including for example text, audio, multimedia, RSS feedinformation can be used without departing from the scope of the instantapplication, which can thereby be communicated and/or accessed andprocessed by the scene-highlight classifier engine 300 according to thesystems and methods discussed herein.

As discussed above, with reference to FIG. 1, the network 315 can be anytype of network such as, but not limited to, a wireless network, a localarea network (LAN), wide area network (WAN), the Internet, or acombination thereof. The network 315 facilitates connectivity of thescene-highlight classifier engine 300, and the database of storedresources 320. Indeed, as illustrated in FIG. 3, the scene-highlightclassifier engine 300 and database 320 can be directly connected by anyknown or to be known method of connecting and/or enabling communicationbetween such devices and resources.

The principal processor, server, or combination of devices thatcomprises hardware programmed in accordance with the special purposefunctions herein is referred to for convenience as scene-highlightclassifier engine 300, and includes scene learning module 302, highlightlearning module 304, scene classifier module 306, highlight classifiermodule 308, and generation module 310. It should be understood that theengine(s) and modules discussed herein are non-exhaustive, as additionalor fewer engines and/or modules (or sub-modules) may be applicable tothe embodiments of the disclosed systems and methods. The operations,configurations and functionalities of each module, and their role withinembodiments of the present disclosure will be discussed with referenceto FIGS. 4A-5.

As discussed in more detail below, the information processed by thescene-highlight classifier engine 300 can be supplied to the database320 in order to ensure that the information housed in the database 320is up-to-date as the disclosed systems and methods leverage real-timeinformation and/or behavior associated with the received streaming videofile, as discussed in more detail below.

Turning to FIGS. 4A-4B and 5, the disclosed processes provide systemsand methods for training a prediction model (e.g., the scene-highlightclassifier engine 300) to score highlights from game scenes of streamingmedia (Process 400 of FIGS. 4A-4B) and implementing the trained model inreal-time on live-streaming video in order to identify and/or createhighlight video segments from the live stream (Process 500 of FIG. 5).

In FIGS. 4A-4B, Process 400 is disclosed which details steps performedin accordance with exemplary embodiments of the present disclosure forbuilding the cascading modeling technique that the scene-highlightclassifier engine 300 will implement upon receiving (e.g., reading)streaming media in real-time. In FIG. 4A, Steps 402-418 of Process 400are disclosed, which are performed by the scene learning module 302,while in FIG. 4B, Steps 420-428 of Process 400 are disclosed, which areperformed by the highlight learning module 304.

As discussed herein, the learned cascading modeling implemented by thescene-highlight classifier engine 300 enables live-streaming videos tobe analyzed, and as a result, non-game parts from video can be discardedearly in the evaluation process, which enables the computationalresources of the scene-highlight classifier engine 300 to be focused ondetecting highlights from game-only scenes. This architectural set upand implementation of the scene-highlight classifier engine 300 enablesefficient, real-time processing of video in a streaming environment.

In order to train the cascaded prediction model implemented by thescene-highlight classifier engine 300, training data for two layers ofpredictions is to be developed: one dataset with scene type labels andanother one with highlight labels for scenes labeled for game-play. Asdiscussed herein, building the dataset for the scene type labelsfacilitates the creation of the dataset for the highlight labels.

In some embodiments, as discussed herein, the trained/learnedscene-highlight classifier engine 300 applies bootstrapping methodologythat enables recursive annotations of scene types that progressivelyminimizes human intervention after each iteration. In some embodiments,the iterations can be performed up to the point where annotators onlyneed to check that the annotations are correct and possibly performminor adjustments. Once the scene type dataset is ready, the “game”sections that have been identified are extracted and delivered to theannotators to create the highlight dataset. In this regard, annotatorsreview the game video segments/scenes and determine which segments arehighlights. In some embodiments, the annotators input/feedback as towhether the scenes are highlights can be provided by the annotatorpressing an arrow (e.g., up for “yes, a highlight” or down for “no, nota highlight”) while they watch the game scene, without needing to labelscene types.

Process 400 begins with Step 402 where a first set of input trainingvideos are received. The set of input training videos can include asingle video or a plurality of videos. Each training video comprisestraining frames. For example, given a set of 100 training videos, Step402 can involve 20 of those 100 training videos (e.g., videos 1-20)being provided to the scene learning module 302.

In Step 404, each received training video from Step 402 is analyzed anda set of training frames within each video is identified. In someembodiments, only a particular set of frames from within each receivedvideo is identified, and in some embodiments, the set of training framesincludes all the frames of the received videos.

In Step 406, a label for each of the identified set of frames isdetermined. In some embodiments, such label can be determined by a humaneditor (referred to as an annotator). In some embodiments, an annotatorwill review the set of frames identified in Step 404, and based on thecontent depicted by each frame, a label can be assigned to that set offrames. The determined/applied label provides an indication as to thetype of content depicted within the set of frames. For example, if theset of frames depicts game play, then the label will indicate that thescene depicted by the set of frames is a “game”—a game label.

In Step 408, a set of instructions is compiled based on the appliedlabel. The set of instructions is stored in connection with the sceneclassifier (in database 320) such that when the scene classifieranalyzes similar type of content as the content of the frames from Steps404-406, the scene classifier can apply the same label.

Now, having an initial set of instructions stored in connection with thescene classifier, the scene classifier is viewed as having been trained(at least for an initial iteration). Therefore, in Step 410, another setof videos are received. As above, the video set received in Step 410 caninclude, for example, another set of 20 unlabeled videos (e.g., videos21-40).

In Step 412, the now trained scene classifier is applied to the new setof videos which results in the automatic determination of labels for theframes of the new videos.

In Step 414, the automatically applied scene labels from Step 412 arereviewed and corrected if necessary. In some embodiments, Step 414 canbe performed by an annotator. For example, if a label is placed in thewrong spot along the sequence of frames of a video, or incorrectlylabeled (e.g., labeled as game play when the commentator is speaking),then the annotator can adjust/modify the label accordingly. In someembodiments, since a label is already applied automatically and theannotator here is only reviewing the annotations accuracy, the playbackof the video scene being reviewed can be increased (e.g., 2×, forexample) since the correct labels may have already been applied (aprobability that increases the more iterations of Step 410-416 areperformed, as discussed below).

In Step 416, a new set of instructions is compiled based on thereview/analysis of the annotator from Step 414. Similar to Step 408,these instructions are stored in connection with the scene classifierand are to be applied to subsequently received video(s). In someembodiments, the storage of instructions comprises updating thepreviously stored instructions with the result of Step 414.

Steps 410-416 are performed recursively until the automatic labelapplication and review process of Steps 412-414 satisfies an accuracythreshold. Therefore, Step 414 further involves comparing theedits/modifications of the automatically applied labels made by theannotator to an accuracy threshold, and should the comparison revealaccuracy below the accuracy threshold, Steps 410-416 are performedagain. For example, if the annotator corrects the automatically learnedand applied scene labels a predetermined number of times, then thatfails the accuracy threshold and another set of videos must be analyzedin order to further train the scene classifier with refined instructions(e.g., Step 416). However, if the accuracy is at or above the accuracythreshold, then Process 400 proceeds to Step 420.

In Step 420, segments labeled with the “game” label from the trainingvideos are extracted. Such extraction can be performed by any known orto be known extraction algorithm that enables the extraction of aportion of a video file to be extracted based on an applied label.

In Step 422, a highlight score each extracted game segment isdetermined. In some embodiments, the highlight scores can be determinedby an annotator. In some embodiments, the annotator performing thehighlight score annotator is a different annotator than the sceneannotator discussed above, and in some embodiments, they can be the sameannotator.

For example, if a game segment is depicting highlight quality content(as discussed above) at or above the highlight threshold, then theannotator can score the game segment a “1.” If the game segment does notdepict a highlight, then the annotator can score the game segment a “0.”In another example, a highlight annotator may score game segments on ascale from 0 to 100, where scores over 75 depict a highlight.

As such, in some embodiments, based on the scores applied by thehighlight annotators in Step 422, a highlight threshold/range can bedetermined. For example, if the annotator scores videos on the scale of0 to 1, as above, then a highlight range can be established fordetermining whether other game segments are highlights based on thewhether they score a “0” or “1.” In another example, from the aboveexample of scoring game segments from 0 to 100, the score of 75 can beset as a highlight threshold, such that any game segment scored at orabove 75 is labeled a highlight.

In Step 426, each extracted game segment is then scored in relation tothe established highlight threshold/range, and based on such scoring, asin Step 428, instructions are stored in connection the highlightclassifier. In a similar manner as discussed above in relation to Steps408 and 416, the stored instructions for the highlight classifier enablefuture game segments to be labeled as a highlight or not, as discussedin more detail in relation to Process 500 of FIG. 5.

Turning to FIG. 5, Processes 500 details steps performed in accordancewith exemplary embodiments of the present disclosure for, in a fullyautomated manner, detecting and rendering highlight video segments ofstreaming game videos in real-time. Steps 502-508 are performed by thescene classifier module 306, which is trained based on the stored sceneinstructions from Process 400, as discussed above. Steps 510-514 areperformed by the highlight classifier module 308, which is trained basedon the stored highlight instructions from Process 400, as discussedabove. Steps 516-518 are performed by the generation module 310.

Process 500 begins with Step 502 where a new streaming video isreceived. As discussed above, the streaming video can be, for example,associated with a live broadcast of a game. Thus, Step 502 can involve,for example, a user visiting a webpage or opening an application to viewa streaming event provided by Yahoo! eSports®. It should be understoodthat the content of the streaming video can be associated with any typeof content, and the functionality of the instant application will remainapplicable.

Step 502's reception of a live-streamed video broadcast includes reading(or storing) the received video frames into memory (e.g., database 320)as each frame of the video is received. In a streaming mediaenvironment, a video is delivered as a continuous stream of short videosegments (e.g., 8 seconds). According to some embodiments, Step 502'sreception of the video stream involves sub-sampling the frames at apredetermined frame rate—for example, 5 frames per second. Therefore,for example, with an 8 second-long video segment there are only 40frames to process.

In Step 504, a set of frames of the received streaming video areautomatically analyzed in order to determine a scene type for theframes. Step 504's analysis of the frames involves accessing the storedframes of the streaming video sequentially. In some embodiments, the setof frames can include one frame at a time, a sub-set or predeterminedsequence of frames within the entirety of the streaming video's frames(a portion of the stream's frames), or all of the frames of thestreaming video.

Thus, in some embodiments, the frames can be read from memory either oneframe at a time, and in some embodiments, the frames can be read inaccordance with a predetermined short sequence of frames. In theembodiments where the frames are read one at a time, scene types can bedetermined based solely on the spatial layout of each frame (e.g., theorder of the frames, as illustrated, for example, in FIG. 6). Inembodiments where a set sequence of frames is read, scene types aredetermined based on the spatial layout of the video stream and thetemporal layout of the video stream. In some embodiments, reading a setsequence of frames can lead to increased performance over analysis via aframe-by-frame analysis; however, an increased computational footprintmay be realized. In either case, the reading of the frame set acts asthe input for the scene classifier module 306, as discussed herein.

In some embodiments, the scene classifier module 306 can implement imagerecognition software to determine (or predict) a scene type. Accordingto some embodiments, the image recognition software implemented by thescene classifier module 306 can involve any known or to be known deeplearning architecture or algorithm, such as, but not limited to, deepneural networks, artificial neural networks (ANNs), convolutional neuralnetworks (CNNs), deep belief networks and the like. According to someembodiments, the scene classifier module 302 employs CNNs (however, itshould not be construed to limit the present disclosure to only theusage of CNNs, as any known or to be known deep learning architecture oralgorithm is applicable to the disclosed systems and methods discussedherein). CNNs consist of multiple layers which can include: theconvolutional layer, ReLU (rectified linear unit) layer, pooling layer,dropout layer and loss layer, as understood by those of skill in theart. When used for image recognition, CNNs produce multiple tiers ofdeep feature collections by analyzing small portions of an input image.

For purposes of this disclosure, such features/descriptors can include,but are not limited to, visual characteristics of the imagescharacterized (or categorized and labeled) by color features, texturefeatures, type features, edge features and/or shape features, and thelike. The results of these collections are then tiled so that theyoverlap to obtain a better representation of the original image; whichis repeated for every CNN layer. CNNs may include local or globalpooling layers, which combine the outputs of feature clusters. Oneadvantage of CNNs is the use of shared weight in convolutional layers;that is, the same filter (weights) is used for each pixel in each layer,thereby reducing required memory size and improving performance.Compared to other image classification algorithms, CNNs use relativelylittle pre-processing which avoids the dependence on prior-knowledge andthe existence of difficult to design handcrafted features.

Indeed, it should be understood by those of skill in the art that thefeatures/attributes (or descriptors or deep descriptors) of the videostream can include any type of information contained in, or associatedtherewith, image data, video data, audio data, multimedia data,metadata, or any other known or to be known content that can beassociated with, derived from or comprised within the streaming videofile. For example, in some embodiments, such feature data can be audiodata associated with an image frame of the video stream that plays whenthe video is viewed.

Thus, in light of the above discussion, Step 504's analysis of the frameset of the streaming video via image recognition software, using CNN forimage classification, involves the scene classifier module 306performing a series of transformations to a frame's image in order toreturn a categorical label as an output. Such transformations caninclude, but are not limited to, numerical transformations of a 2Dconvolution for an image (or single frame), 3D convolution for asequence of images (or set sequence of frames), average/max pooling overlocal regions in space and time, local response normalization, and thelike. As discussed above, implementation of a CNN image classificationembodiment involves multiple layers that represent an input at anincreasing level of abstraction in a fine-to-coarse manner. For example,a low-level layer can represent an input image (from a frame) asactivations to several 3×3 edge filters, while a high-level filter mayrepresent the input image as activations to several 32×32 object-likeshape filters. The CNN classification can then include a last layer thatproduces a categorical label. Such layer can include any type ofclassification technique or algorithm, such as, for example, a softmaxfunction followed by an argmax operation.

Therefore, as a result of the analysis performed in Step 504, asdetailed above, a label can be applied to each scene of the streamingvideo. Step 506. As discussed above, for example, such labels caninvolve categorizing scenes as, for example, “game,” “game play,” “gamecharacter selection,” “game statistics,” “game player,” “commentator,”“audience,” “game statistics,” and the like, or any other type ofcategorical summarization of a scene within a game's video stream. Asdiscussed above, such scene label types can be initially determined fromthe scene learning module 302 and are applied by the scene classifiermodule 306.

In some embodiments, once the scene labels for the video segments of thestreaming video are determined, the scene classifier module 306 mayexecute temporal smoothing software in order to reduce noise of thescene type results. Such temporal smoothing software can involveperforming any type of known or to be known temporal smoothing techniqueor algorithm including, but not limited to, additive smoothing,convolution, curve fitting, edge preserving smoothing, exponentialsmoothing, and the like, to name a few examples.

In Step 508, a determination is made regarding whether the labeledscenes are “game” scenes. That is, once the labels are applied to ascene or scenes of a streaming video, it is determined whether the scenedepicts game play or other type of scenes that appear in the videostream (e.g., scenes depicting commentator, the audience, gamestatistics, or any other type of scene from a game that is not directlyshowing game play or activity).

If the scene is a “game” scene—it is labeled as a “game” scene from Step506—then, Process 500 proceeds to Step 510 where a score for the gamescene is determined.

In Step 510, in some embodiments, only game scenes are scored becausethe scene-highlight classifier engine 300 is implemented to determine“highlights” of game play scenes. In some embodiments, the scoring ofthe game scenes is performed by the highlight classifier module 308implementing any known or to be known image recognition model in orderto determine a highlight score.

In a similar manner as discussed above, the frames of the scene that arelabeled as “game” scenes are read from memory either in a frame-by-framebasis or as a set sequence of frames (see Step 504 above). Similar toStep 506, in some embodiments, the highlight classifier module 308implements a CNN image classification model to analyze the contents ofthe game scene (e.g., frame or frames of the scene); however, thedifference between Step 506 and Step 510 analysis is that the last layerof the CNN model produces a real-valued scalar range that represents ahighlight score (as opposed to a label). In some embodiments, forexample, Step 510 can involve the softmax function of the last layerbeing followed by a max operation (as opposed to an argmax operation).In some embodiments, in another example, the last layer of the CNN modelimplemented by the highlight classifier module 308 can implement aregression-type function using any known or to be known regression orregression-type technique or algorithm to produce a score for content ofa game scene. In some embodiments, the scalar range (or threshold) canbe initially determined by the highlight learning module 304 and appliedby the highlight classifier module 308, as discussed above.

In Step 512, once the scores are determined, they are compared againstthe scalar range/threshold in order to determine if the game scene is ahighlight. In Step 514, if the game scene's score falls within thescalar range (e.g., [0, 1]), or satisfies the highlight threshold, thenthe scene is labeled as a “highlight.” Process 500 then proceeds to Step516.

In some embodiments, once the highlight labels for the video segments ofthe streaming video labeled as game scenes are determined, the highlightclassifier module 308 may execute temporal smoothing software in orderto reduce noise, in a similar manner as discussed above.

Turning back to Step 508, if the game scene is determined to be anothertype of scene—i.e., not a “game” scene—a highlight score of zero isassigned to such scene and Process 500 proceeds to Step 516.

In Step 516, an output file is generated (or created) and stored inmemory (e.g., database 320). The generated output file for the streamingvideo comprises time-stamped information associated with the determinedand assigned scene labels and highlight labels. Such information caninclude, but is not limited to, a frame index, scene type label, scenelabel accuracy (or confidence), highlight score, and the like.

By way of a non-limiting example of Steps 502-516, using the videostream 600 from FIG. 6 as discussed above, an output file for stream 600is generated that comprises the following information, as illustrated inthe below table:

Frame Index Scene Type Highlight Score 1-3 Commentator 0 4 Transition 05-7 Game 1 8 Transition 0

It should be understood that such table is a non-limiting example of agenerated output file for stream 600, and should not be construed aslimiting the scope of the output file or information that can be storedin the output file.

In Step 518, the game segments labeled as highlight game segments (e.g.,frames 5-7 of stream 600 from the above example) can be transformed intotheir own independent short-form files. For example, the generationmodule 310 can create an animated GIF from a highlight game segmentusing any known or to be known frame/segment transformation technique,such as, but not limited to, imagemagick and gifsicle libraries, to namea few examples. Generation of a short-form video, as discussed herein,can include extracting the frames from memory, copying the frames frommemory and/or creating new frames based on the content of the storedframes, and the like.

In some embodiments, after the short-form generation of the highlightvideo segment is performed, the generated video file can be communicatedto a user for display on a user's device. In some embodiments, suchcommunication can involve automatically rendering the highlight videosegment upon display on the user's device, which is ideal for a userthat has requested the highlight video segment. In some embodiments,such communication can involve a user sharing the highlight videosegment with another user. In some embodiments, sharing of the highlightvideo segment with an identified set of users can be performedautomatically upon generation of the highlight video segment, where notonly does the requesting user receive the highlight video segment, butalso other users who follow the user, or have been identified by theuser, can be provided the generated highlight video segment (e.g.,reblogging the highlight video segment to a user's followers pages onTumblr®). As will be understood by those of skill in the art, sharinghighlight video segment extracted from streaming video in this mannercould result in improved user engagement in video content from which thehighlight video segment was created, as well as increased activity byusers on the site/platform (e.g., Yahoo! eSports®) associated with thestreaming video/highlight video segment.

According to some embodiments of the present disclosure, informationassociated with a extracted/created highlight video segment, asdiscussed above in relation to Process 500 (and/or Process 400), can befed back to the scene-highlight classifier engine 300 for modeling (ortraining) of the information stored in database 320 via iterative orrecursive bootstrapping or aggregation functionality. This can improvethe accuracy of scores for highlight video segments, as discussed above.Embodiments of the present disclosure involve the scene-highlightclassifier engine 300 applying such recursive/bootstrapping functionsutilizing any known or to be known open source and/or commercialsoftware machine learning algorithm, technique or technology.

FIG. 7 is a work flow example 700 for serving relevant digital contentcomprising advertisements (e.g., advertisement content) based on theinformation associated with an identified and/or created highlight videosegment, as discussed above in relation to FIGS. 3-5. Such information,referred to as “highlight video segment information” for referencepurposes only, can include, but is not limited to, the identity of thevideo segment within the streaming media (e.g., frames and labels), theattributes of the video segment, the content of the video segment, andthe like, and/or some combination thereof.

As discussed herein, reference to an “advertisement” should beunderstood to include, but not be limited to, digital content thatprovides information provided by another user, service, third party,entity, and the like. Such digital ad content can include any type ofmedia renderable by a computing device, including, but not limited to,video, text, audio, images, and/or any other type of known or to beknown multi-media. In some embodiments, the digital ad content can beformatted as hyperlinked multi-media content that provides deep-linkingfeatures and/or capabilities.

By way of a non-limiting example, work flow 700 includes a user beingprovided with a highlight video segment from a recent contest ofStarCraft on the Yahoo! eSports® platform, as discussed above. Based oninformation related to the determination that the highlight videosegment is derived from the StarCraft game, for example, the user may beprovided with digital ad content related to the purchase of accessoriesfrom playing the StarCraft game. In another example, the digital adcontent can be related to coupons for locations that sell StarCraft orother like games. In yet another non-limiting example, the digital adcontent can be related to promotions provided by Yahoo! ® for the userto set up or upgrade his/her account status within the eSports®platform.

In Step 702, highlight video segment information associated with acreated highlight video segment file is identified. As discussed above,the highlight video segment information can be based on the highlightvideo segment creation process outlined above with respect to FIGS. 3-5.For purposes of this disclosure, Process 700 will refer to singlehighlight video segment file as the basis for serving anadvertisement(s); however, it should not be construed as limiting, asany number of highlight video segments, and/or quantities of informationrelated to users and their interaction with created highlight videosegments or streaming media can form such basis, without departing fromthe scope of the instant disclosure.

In Step 704, a context is determined based on the identified highlightvideo segment information. This context forms a basis for servingadvertisements related to the highlight video segment information. Insome embodiments, the context can be determined by determining acategory which the highlight video segment information of Step 702represents. For example, the category can be related to the type ofstreaming video from which the highlight video segment was created,and/or can be related to the content type of the highlight video segmentfile. In some embodiments, the identification of the context from Step704 can occur before, during and/or after the analysis detailed abovewith respect to Processes 400-500, or some combination thereof.

In Step 706, the context (e.g., content/context data) is communicated(or shared) with an advertisement platform comprising an advertisementserver 130 and ad database. Upon receipt of the context, theadvertisement server 130 performs a search for a relevant advertisementwithin the associated ad database. The search for an advertisement isbased at least on the identified context.

In Step 708, the advertisement server 130 searches the ad database foran advertisement(s) that matches the identified context. In Step 710, anadvertisement is selected (or retrieved) based on the results of Step708. In some embodiments, the selected advertisement can be modified toconform to attributes of the page, message or method upon which theadvertisement will be displayed, and/or to the application and/or devicefor which it will be displayed. In some embodiments, the selectedadvertisement is shared or communicated via the application the user isutilizing to render the highlight video segment. Step 712. In someembodiments, the selected advertisement is sent directly to each user'scomputing device. In some embodiments, the selected advertisement isdisplayed in conjunction with a displayed highlight video segment on theuser's device and/or within the application being used to identify,select and/or render the highlight video segment file.

As shown in FIG. 8, internal architecture 800 of a computing device(s),computing system, computing platform, user devices, set-top box, smartTV and the like includes one or more processing units, processors, orprocessing cores, (also referred to herein as CPUs) 812, which interfacewith at least one computer bus 802. Also interfacing with computer bus802 are computer-readable medium, or media, 806, network interface 814,memory 804, e.g., random access memory (RAM), run-time transient memory,read only memory (ROM), media disk drive interface 820 as an interfacefor a drive that can read and/or write to media including removablemedia such as floppy, CD-ROM, DVD, media, display interface 810 asinterface for a monitor or other display device, keyboard interface 816as interface for a keyboard, pointing device interface 818 as aninterface for a mouse or other pointing device, and miscellaneous otherinterfaces not shown individually, such as parallel and serial portinterfaces and a universal serial bus (USB) interface.

Memory 804 interfaces with computer bus 802 so as to provide informationstored in memory 804 to CPU 812 during execution of software programssuch as an operating system, application programs, device drivers, andsoftware modules that comprise program code, and/or computer executableprocess steps, incorporating functionality described herein, e.g., oneor more of process flows described herein. CPU 812 first loads computerexecutable process steps from storage, e.g., memory 804, computerreadable storage medium/media 806, removable media drive, and/or otherstorage device. CPU 812 can then execute the stored process steps inorder to execute the loaded computer-executable process steps. Storeddata, e.g., data stored by a storage device, can be accessed by CPU 812during the execution of computer-executable process steps.

Persistent storage, e.g., medium/media 806, can be used to store anoperating system and one or more application programs. Persistentstorage can also be used to store device drivers, such as one or more ofa digital camera driver, monitor driver, printer driver, scanner driver,or other device drivers, web pages, content files, playlists and otherfiles. Persistent storage can further include program modules and datafiles used to implement one or more embodiments of the presentdisclosure, e.g., listing selection module(s), targeting informationcollection module(s), and listing notification module(s), thefunctionality and use of which in the implementation of the presentdisclosure are discussed in detail herein.

Network link 828 typically provides information communication usingtransmission media through one or more networks to other devices thatuse or process the information. For example, network link 828 mayprovide a connection through local network 824 to a host computer 826 orto equipment operated by a Network or Internet Service Provider (ISP)830. ISP equipment in turn provides data communication services throughthe public, worldwide packet-switching communication network of networksnow commonly referred to as the Internet 832.

A computer called a server host 834 connected to the Internet 832 hostsa process that provides a service in response to information receivedover the Internet 832. For example, server host 834 hosts a process thatprovides information representing video data for presentation at display810. It is contemplated that the components of system 800 can bedeployed in various configurations within other computer systems, e.g.,host and server.

At least some embodiments of the present disclosure are related to theuse of computer system 800 for implementing some or all of thetechniques described herein. According to one embodiment, thosetechniques are performed by computer system 800 in response toprocessing unit 812 executing one or more sequences of one or moreprocessor instructions contained in memory 804. Such instructions, alsocalled computer instructions, software and program code, may be readinto memory 804 from another computer-readable medium 806 such asstorage device or network link. Execution of the sequences ofinstructions contained in memory 804 causes processing unit 812 toperform one or more of the method steps described herein. In alternativeembodiments, hardware, such as ASIC, may be used in place of or incombination with software. Thus, embodiments of the present disclosureare not limited to any specific combination of hardware and software,unless otherwise explicitly stated herein.

The signals transmitted over network link and other networks throughcommunications interface, carry information to and from computer system800. Computer system 800 can send and receive information, includingprogram code, through the networks, among others, through network linkand communications interface. In an example using the Internet, a serverhost transmits program code for a particular application, requested by amessage sent from computer, through Internet, ISP equipment, localnetwork and communications interface. The received code may be executedby processor 802 as it is received, or may be stored in memory 804 or instorage device or other non-volatile storage for later execution, orboth.

For the purposes of this disclosure a module is a software, hardware, orfirmware (or combinations thereof) system, process or functionality, orcomponent thereof, that performs or facilitates the processes, features,and/or functions described herein (with or without human interaction oraugmentation). A module can include sub-modules. Software components ofa module may be stored on a computer readable medium for execution by aprocessor. Modules may be integral to one or more servers, or be loadedand executed by one or more servers. One or more modules may be groupedinto an engine or an application.

For the purposes of this disclosure the term “user”, “subscriber”“consumer” or “customer” should be understood to refer to a user of anapplication or applications as described herein and/or a consumer ofdata supplied by a data provider. By way of example, and not limitation,the term “user” or “subscriber” can refer to a person who receives dataprovided by the data or service provider over the Internet in a browsersession, or can refer to an automated software application whichreceives the data and stores or processes the data.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure may be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, may be distributed among softwareapplications at either the client level or server level or both. In thisregard, any number of the features of the different embodimentsdescribed herein may be combined into single or multiple embodiments,and alternate embodiments having fewer than, or more than, all of thefeatures described herein are possible.

Functionality may also be, in whole or in part, distributed amongmultiple components, in manners now known or to become known. Thus,myriad software/hardware/firmware combinations are possible in achievingthe functions, features, interfaces and preferences described herein.Moreover, the scope of the present disclosure covers conventionallyknown manners for carrying out the described features and functions andinterfaces, as well as those variations and modifications that may bemade to the hardware or software or firmware components described hereinas would be understood by those skilled in the art now and hereafter.

Furthermore, the embodiments of methods presented and described asflowcharts in this disclosure are provided by way of example in order toprovide a more complete understanding of the technology. The disclosedmethods are not limited to the operations and logical flow presentedherein. Alternative embodiments are contemplated in which the order ofthe various operations is altered and in which sub-operations describedas being part of a larger operation are performed independently.

While various embodiments have been described for purposes of thisdisclosure, such embodiments should not be deemed to limit the teachingof this disclosure to those embodiments. Various changes andmodifications may be made to the elements and operations described aboveto obtain a result that remains within the scope of the systems andprocesses described in this disclosure.

What is claimed is:
 1. A method comprising steps of: receiving, at acomputing device, a video stream comprising a plurality of frames ofcontent; determining, via the computing device, labels for the pluralityof frames based on attributes of the plurality of frames, each labelcomprising an indication of a scene type depicted in content of arespective frame; storing, via the computing device, the labels astraining data for first image recognition software; training, by thecomputing device, the first image recognition software using thetraining data; applying, via the computing device, the first imagerecognition software to a set of training videos, the first imagerecognition software generating second labels for the training videos;identifying, via the computing device, an inaccurate label in the secondlabels; updating, via the computing device, the indication associatedwith the inaccurate label; updating, via the computing device, thetraining data with the updated label; and re-training, via the computingdevice, the first image recognition using the training data.
 2. Themethod of claim 1, wherein when said inaccurate label fails to satisfyan accuracy threshold, repeating said steps until said inaccurate labelsatisfies the accuracy threshold.
 3. The method of claim 1, wherein whensaid accuracy satisfies said accuracy threshold, the method furthercomprises: determining a highlight score for each scene of the trainingvideos labeled a game scene; labeling each game scene a highlight basedon said highlight score; and storing said labeled game scenes in saiddatabase for use by second image recognition software, wherein saidlabeled game scenes are associated with said predetermined set ofmachine-learned highlight attributes.
 4. The method of claim 1, whereinthe analysis of the plurality of frames is based on a spatial layout ofthe received frames of the video stream.
 5. The method of claim 1,wherein said analysis of the plurality of frames is based on a spatiallayout of the received frames and a temporal layout of the receivedframes.
 6. The method of claim 1, wherein said transformations performedby the first image recognition software comprise convolutional neuralnetwork (CNN) image classification, wherein said CNN imageclassification comprises a last layer having a softmax function and anargmax operation.
 7. The method of claim 1, further comprising:determining, via the computing device, a type of scene depicted in a newframe set, the determining comprising applying the first imagerecognition software to frames of the new frame set to assign a scenelabel to said new frame set; determining, via the computing device, thatsaid scene type is a game scene based on the assigned label, said gamescene comprising content associated with game play occurring in a liveevent; determining, via the computing device, that said content withinsaid game scene is a highlight and designating said game scene as ahighlight, said determination comprising computing a highlight score forsaid game scene by analyzing frames of the game scene via second imagerecognition software and determining that an output from said secondimage recognition software satisfies a threshold, said output based on acomparison of the attributes of the frame content in the game sceneagainst a predetermined set of machine-learned highlight attributes;generating, via the computing device, an output file corresponding tothe video stream, said output file comprising time-stamped informationassociated with the scene label and the highlight label; andautomatically creating, via the computing device, a highlight videosegment from the video stream based on said output file, said highlightvideo segment created from and comprising frames of the video streamidentified in the output file as the game scene and highlight.
 8. Themethod of claim 1, further comprising: storing each received frame ofthe video stream in memory as it is received; and identifying saidplurality of frames from said stored received frames.
 9. The method ofclaim 7, further comprising: determining a context of the highlightvideo segment; causing communication, over the network, of said contextto an advertisement platform to obtain digital advertisement contentassociated with said context; and communicating a digital content objectcomprising said identified digital advertisement content with saidhighlight video segment to a user.
 10. A non-transitorycomputer-readable storage medium tangibly encoded withcomputer-executable instructions, that when executed by a processorassociated with a computing device, performs a method comprising:receiving a video stream comprising a plurality of frames of content;determining labels for the plurality of frames based on attributes ofthe plurality of frames, each label comprising an indication of a scenetype depicted in content of a respective frame; storing the labels astraining data for first image recognition software; training the firstimage recognition software using the training data; applying the firstimage recognition software to a set of training videos, the first imagerecognition software generating second labels for the training videos;identifying an inaccurate label in the second labels; updating theindication associated with the inaccurate label; updating the trainingdata with the updated label; and re-training the first image recognitionusing the training data.
 11. The non-transitory computer-readablestorage medium of claim 10, the method further comprising: determiningan accuracy of said automatically applied labels; when said accuracyfails to satisfy an accuracy threshold, re-adjusting labels identifiedas inaccurate until said accuracy satisfies the accuracy threshold, andwhen said accuracy satisfies said accuracy threshold: determining ahighlight score for each scene of the training videos labeled a gamescene; labeling each game scene a highlight based on said highlightscore; and storing said labeled game scenes in said database for use bysecond image recognition software.
 12. The non-transitorycomputer-readable storage medium of claim 10, further comprising:storing each received frame of the video stream in memory as it isreceived; and identifying said plurality of frames from said storedreceived frames.
 13. A computing device comprising: a processor; anon-transitory computer-readable storage medium for tangibly storingthereon program logic for execution by the processor, the program logiccomprising: logic executed by the processor for receiving, at acomputing device, a video stream comprising a plurality of frames ofcontent; logic, executed by the processor, for determining labels forthe plurality of frames based on attributes of the plurality of frames,each label comprising an indication of a scene type depicted in contentof a respective frame; logic, executed by the processor, for storing thelabels as training data for first image recognition software; logic,executed by the processor, for training the first image recognitionsoftware using the training data; logic, executed by the processor, forapplying the first image recognition software to a set of trainingvideos, the first image recognition software generating second labelsfor the training videos; logic, executed by the processor, foridentifying an inaccurate label in the second labels; logic, executed bythe processor, for updating the indication associated with theinaccurate label; and logic, executed by the processor, for updating thetraining data with the updated label; and logic, executed by theprocessor, for re-training the first image recognition using thetraining data.
 14. The method of claim 7, wherein the second imagerecognition software comprises convolutional neural network (CNN) imageclassification, wherein said CNN image classification comprises a lastlayer comprising functionality selected from a group consisting of: asoftmax function and max operation, and a regression-type function. 15.The method of claim 7, further comprising: communicating, over thenetwork, said highlight video segment to a user, said communicationoccurring automatically upon creation of said highlight video segment;determining that said type of scene is a scene type different from saidgame scene; assigning said frame set a highlight score of zero indictingthat the scene is not a highlight; and storing information associatedwith said different scene type and zero highlight score in said outputfile.
 16. The method of claim 7, the first image recognition softwarecomprising a deep learning algorithm comprising multiple layers, eachlayer producing a feature collection for a given frame, the deeplearning algorithm comprising overlapping, for each frame, the featurecollections associated with each layer, pooling the overlapping featurecollections, and assigning a label to the frame set based on the pooledfeature collections.
 17. The non-transitory computer-readable storagemedium of claim 10, the method further comprising: determining, via thecomputing device, a type of scene depicted in a new frame set, thedetermining comprising applying the first image recognition software toframes of the new frame set to assign a scene label to said new frameset; determining, via the computing device, that said scene type is agame scene based on the assigned label, said game scene comprisingcontent associated with game play occurring in a live event;determining, via the computing device, that said content within saidgame scene is a highlight and designating said game scene as ahighlight, said determination comprising computing a highlight score forsaid game scene by analyzing frames of the game scene via second imagerecognition software and determining that an output from said secondimage recognition software satisfies a threshold, said output based on acomparison of the attributes of the frame content in the game sceneagainst a predetermined set of machine-learned highlight attributes;generating, via the computing device, an output file corresponding tothe video stream, said output file comprising time-stamped informationassociated with the scene label and the highlight label; andautomatically creating, via the computing device, a highlight videosegment from the video stream based on said output file, said highlightvideo segment created from and comprising frames of the video streamidentified in the output file as the game scene and highlight.
 18. Thenon-transitory computer-readable storage medium of claim 17, the methodfurther comprising: communicating, over the network, said highlightvideo segment to a user, said communication occurring automatically uponcreation of said highlight video segment; determining that said type ofscene is a scene type different from said game scene; assigning saidframe set a highlight score of zero indicting that the scene is not ahighlight; and storing information associated with said different scenetype and zero highlight score in said output file.
 19. Thenon-transitory computer-readable storage medium of claim 17, the firstimage recognition software comprising a deep learning algorithmcomprising multiple layers, each layer producing a feature collectionfor a given frame, the deep learning algorithm comprising overlapping,for each frame, the feature collections associated with each layer,pooling the overlapping feature collections, and assigning a label tothe frame set based on the pooled feature collections.